In the past decade, we have seen a trend in wireless communications from supporting only voice and low-rate data services towards supporting high-rate multimedia applications. To support this high demand on data rate, the bandwidth of modern wireless communication systems is normally in the order of tens of MHz. Because of this large bandwidth, the communication channels between the transmitter and the receiver exhibit different responses at different frequencies, and are called frequency-selective fading channels. The Orthogonal Frequency Division Multiplexing (OFDM) system provides an efficient and robust solution for communication over frequency-selective fading channels and has been adopted in various wireless communication standards. The multiple-input and multiple-out (MIMO) OFDM system further increases the data rates and robustness of the OFDM system by using multiple transmit and receive antennas. The multi-user MIMO-OFDM system is an extension of the MIMO-OFDM system to a multi-user context. It enables transmission and reception of information from multiple users at the same time and in the same frequency band. A common drawback of all these wireless OFDM systems is their sensitivity to frequency synchronization errors in the form of carrier frequency offset (CFO). CFO is an offset between the carrier frequency of the transmitted signal and the carrier frequency used at the receiver for demodulation. It is caused by the mismatch between the transmitter and receiver local oscillators and, in case of moving transmitters and/or receivers, also by the Doppler effect of the channel. In OFDM systems, CFO causes inter-carrier interference (ICI), which can be several orders larger than the noise sources in the system. Thus, accurate CFO estimation, through frequency synchronization, is essential for ensuring adequate performance of OFDM systems. To this end, many CFO estimation and compensation algorithms have been described in the literature for a variety of wireless OFDM systems. These algorithms can be broadly divided into two categories, namely blind algorithms and training-based algorithms. For blind CFO estimation algorithms, CFO is estimated using the statistical properties of the received signal only, without explicit knowledge of the transmitted signal. For training-based CFO estimation algorithms, specially designed training signals known to the receiver are transmitted to assist the receiver in estimating the CFO. A key drawback of blind algorithms is their high computational complexity. In this thesis, we address this drawback by developing a particular type of low-complexity blind CFO estimation algorithms in the context of single-input single-output (SISO) OFDM systems. For training-based algorithms, the computational complexity is normally low because training sequences can be designed to limit the required computations at the receiver. A key drawback of training-based algorithms is the training overhead from the transmission of training sequences, as it reduces the effective data throughput of the system. Comparing to SISO-OFDM systems, the training overhead for MIMO-OFDM systems is even larger. To address this drawback, in this thesis, we propose an efficient training sequence design for MIMO-OFDM systems, which has low training overhead and at the same time permits low-complexity maximum-likelihood joint CFO and channel estimation.
|Qualification||Doctor of Philosophy|
|Award date||23 Nov 2009|
|Place of Publication||Eindhoven|
|Publication status||Published - 2009|